High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators
Machine learning (ML) has emerged as an effective approach for optimizing circuit design and bringing a paradigm shift in the development of wireless power transfer (WPT) systems. Being the main building blocks of near-field WPT, the slotted ground plane (SGP) resonators with a high quality factor (...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10902135/ |
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| author | K. Dautov G. Tolebi M. S. Hashmi A. Jarndal E. Almajali G. Nauryzbayev |
| author_facet | K. Dautov G. Tolebi M. S. Hashmi A. Jarndal E. Almajali G. Nauryzbayev |
| author_sort | K. Dautov |
| collection | DOAJ |
| description | Machine learning (ML) has emerged as an effective approach for optimizing circuit design and bringing a paradigm shift in the development of wireless power transfer (WPT) systems. Being the main building blocks of near-field WPT, the slotted ground plane (SGP) resonators with a high quality factor (Q) enhance power transfer efficiency. However, it is pertinent to note that the resonator size, slot shape, and location result in distinct Q outcomes. Therefore, this work delves into the use of ML for predicting Q of SGP resonators. It can be predicted through a deep learning approach, owing to its capacity to learn from the implicit associations between input and output data. Hence, a deep neural network (DNN) model was designed using 20006 data files generated by electromagnetic (EM) simulations. DNN demonstrated its effectiveness, achieving an accuracy of 99.26%, thereby outperforming other benchmark ML models. Furthermore, the model proved its robustness in predicting Q of variously sized resonators and showed 98.3% accuracy. Subsequently, this enables anticipating the Q metric of scaled resonators without the need for exhaustive EM simulations. The predicted Q values were supported through experimental measurements. Finally, the SGP resonators were aptly employed to exhibit the near-field WPT system. |
| format | Article |
| id | doaj-art-ec136f12900946c4b4a0f5e4d4efb5cf |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ec136f12900946c4b4a0f5e4d4efb5cf2025-08-20T03:00:01ZengIEEEIEEE Access2169-35362025-01-0113366473665710.1109/ACCESS.2025.354514110902135High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground ResonatorsK. Dautov0G. Tolebi1https://orcid.org/0000-0001-8181-6965M. S. Hashmi2https://orcid.org/0000-0002-1772-588XA. Jarndal3https://orcid.org/0000-0002-1873-2088E. Almajali4https://orcid.org/0000-0003-1289-7903G. Nauryzbayev5https://orcid.org/0000-0003-4470-3851Research Institute of Sciences and Engineering (RISE), University of Sharjah, Sharjah, United Arab EmiratesSchool of Engineering and Digital Sciences, Nazarbayev University, Astana, KazakhstanSchool of Engineering and Digital Sciences, Nazarbayev University, Astana, KazakhstanDepartment of Electrical Engineering, University of Sharjah, Sharjah, United Arab EmiratesDepartment of Electrical Engineering, University of Sharjah, Sharjah, United Arab EmiratesSchool of Engineering and Digital Sciences, Nazarbayev University, Astana, KazakhstanMachine learning (ML) has emerged as an effective approach for optimizing circuit design and bringing a paradigm shift in the development of wireless power transfer (WPT) systems. Being the main building blocks of near-field WPT, the slotted ground plane (SGP) resonators with a high quality factor (Q) enhance power transfer efficiency. However, it is pertinent to note that the resonator size, slot shape, and location result in distinct Q outcomes. Therefore, this work delves into the use of ML for predicting Q of SGP resonators. It can be predicted through a deep learning approach, owing to its capacity to learn from the implicit associations between input and output data. Hence, a deep neural network (DNN) model was designed using 20006 data files generated by electromagnetic (EM) simulations. DNN demonstrated its effectiveness, achieving an accuracy of 99.26%, thereby outperforming other benchmark ML models. Furthermore, the model proved its robustness in predicting Q of variously sized resonators and showed 98.3% accuracy. Subsequently, this enables anticipating the Q metric of scaled resonators without the need for exhaustive EM simulations. The predicted Q values were supported through experimental measurements. Finally, the SGP resonators were aptly employed to exhibit the near-field WPT system.https://ieeexplore.ieee.org/document/10902135/Average comparative error (ACE)deep neural network (DNN)machine learning (ML)magnetic resonant coupling (MRC)resonatorslotted ground plane (SGP) |
| spellingShingle | K. Dautov G. Tolebi M. S. Hashmi A. Jarndal E. Almajali G. Nauryzbayev High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators IEEE Access Average comparative error (ACE) deep neural network (DNN) machine learning (ML) magnetic resonant coupling (MRC) resonator slotted ground plane (SGP) |
| title | High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators |
| title_full | High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators |
| title_fullStr | High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators |
| title_full_unstemmed | High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators |
| title_short | High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators |
| title_sort | high precision and robust dnn model for predicting quality factor of wpt oriented slotted ground resonators |
| topic | Average comparative error (ACE) deep neural network (DNN) machine learning (ML) magnetic resonant coupling (MRC) resonator slotted ground plane (SGP) |
| url | https://ieeexplore.ieee.org/document/10902135/ |
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